RESEARCH ARTICLE

Age-Related 1H NMR Characterization of Cerebrospinal Fluid in Newborn and Young Healthy Piglets Domenico Ventrella1☯*, Luca Laghi2☯, Francesca Barone1, Alberto Elmi1, Noemi Romagnoli1, Maria Laura Bacci1 1 Department of Veterinary Medical Sciences, University of Bologna, Bologna, Italy, 2 Centre of Foodomics, Department of Agro-Food Science and Technology, University of Bologna, Bologna, Italy ☯ These authors contributed equally to this work. * [email protected]

Abstract a11111

OPEN ACCESS Citation: Ventrella D, Laghi L, Barone F, Elmi A, Romagnoli N, Bacci ML (2016) Age-Related 1H NMR Characterization of Cerebrospinal Fluid in Newborn and Young Healthy Piglets. PLoS ONE 11(7): e0157623. doi:10.1371/journal.pone.0157623 Editor: Richard H Barton, Imperial College London, UNITED KINGDOM Received: February 11, 2016 Accepted: June 2, 2016 Published: July 8, 2016 Copyright: © 2016 Ventrella et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data are within the paper and its Supporting Information files.

When it comes to neuroscience, pigs represent an important animal model due to their resemblance with humans’ brains for several patterns including anatomy and developmental stages. Cerebrospinal fluid (CSF) is a relatively easy-to-collect specimen that can provide important information about neurological health and function, proving its importance as both a diagnostic and biomedical monitoring tool. Consequently, it would be of high scientific interest and value to obtain more standard physiological information regarding its composition and dynamics for both swine pathology and the refinement of experimental protocols. Recently, proton nuclear magnetic resonance (1H NMR) spectroscopy has been applied in order to analyze the metabolomic profile of this biological fluid, and results showed the technique to be highly reproducible and reliable. The aim of the present study was to investigate in both qualitative and quantitative manner the composition of Cerebrospinal Fluid harvested form healthy newborn (5 days old-P5) and young (30-P30 and 50P50 days old) piglets using 1H NMR Spectroscopy, and to analyze any possible difference in metabolites concentration between age groups, related to age and Blood-Brain-Barrier maturation. On each of the analyzed samples, 30 molecules could be observed above their limit of quantification, accounting for 95–98% of the total area of the spectra. The concentrations of adenine, tyrosine, leucine, valine, 3-hydroxyvalerate, 3-methyl-2-oxovalerate were found to decrease between P05 and P50, while the concentrations of glutamine, creatinine, methanol, trimethylamine and myo-inositol were found to increase. The P05-P30 comparison was also significant for glutamine, creatinine, adenine, tyrosine, leucine, valine, 3hydroxyisovalerate, 3-methyl-2-oxovalerate, while for the P30-P50 comparison we found significant differences for glutamine, myo-inositol, leucine and trimethylamine. None of these molecules showed at P30 concentrations outside the P05 –P50 range.

Funding: This study was supported by RFO 60%, Ateneo di Bologna. Competing Interests: The authors have declared that no competing interests exist.

PLOS ONE | DOI:10.1371/journal.pone.0157623 July 8, 2016

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Introduction Pigs represent an important animal model, being phylogenetically similar to primates [1], therefore extremely similar to humans, especially when compared to other models such as the murine one [2]. It is therefore necessary and mandatory to acquire as much knowledge as possible regarding porcine genetics and physiology in order to create specific models for each pathology and understand its correlation to its human analogue. When it comes to neuroscience, the porcine brain resembles the human brain in terms of weight, volume, cortical surface area, myelination, composition and electrical activity, and its development, just like in humans, extends from prenatal to early postnatal life [3]. Throughout the years, several porcine models carrying gene variants that cause neurological pathologies in men have been created [3] validating and proving the importance of this species in the laboratory and translational medicine. Due to its position and fragility, Central Nervous System (CNS) samples can be hard to collect and the procedure may lead to severe damage, but Cerebrospinal fluid (CSF) represents a relatively easy to collect specimens that can provide important information about neurological health and function [4]. CSF functions include regulation of the intracranial pressure (ICP), regulation of the chemical environment of the CNS and intracerebral transport [5]. CSF is the product of plasma ultrafiltration and membrane secretion, usually clear and colorless [5]. It is nearly acellular, and does not contain erythrocytes in physiological conditions [6]. On average, dogs and cats have from 0 to 2 cells/μl, with specific normal nucleated cell count ranges for different species [7]. Protein concentration is usually very low: canine CSF samples usually show 10-40mg/dl of proteins compared to 5–7 g/dl in serum, the majority of which is represented by albumin (50–70%) [5]. Its production and absorption are the result of the interaction of several interfaces such as the Blood-Brain-Barrier (BBB) and the blood-CSF barrier [8], therefore it can vary depending on the age and the maturation of the above-mentioned barriers. Recently, canine CSF small organic molecules profile, referred to as metabolome [9], was investigated using proton nuclear magnetic resonance (1H NMR) spectroscopy [10], in order to outline a fingerprint of healthy status useful for designing and interpreting clinical trials. 1H NMR is indeed ideally tailored for metabolomics investigations on biofluids, due to its high reproducibility, its intrinsic quantitative nature and the minimum sample preparation required [11]. Investigations of this kind have been, in the recent past, precious for characterizing diseases [12–13] and inflammation conditions [14]. In addition, focusing on rats, it was proven that CSF metabolomics can reveal changes in CNS metabolism in key conditions, strongly suggesting that interesting insights of CNS metabolism can be obtained also during animal growth [15]. In order for these investigations to be effective, a key role is covered by the exploration of the widest possible portions of the metabolome space [11], given by the number of quantified molecules and the by the knowledge about the connection between the metabolome profile and natural fluctuations of the physiological status, such as those connected to ageing. Regarding the swine metabolome, the characterization of urine, serum, liver and kidney metabolome was recently performed, using both one and two-dimensional 1H and 13C nuclear magnetic resonance spectroscopy (NMR) and high-resolution magic angle spinning (HR-MAS) NMR [16]. The study provided valuable information for translational medicine, validating once again the importance of metabolomics. The aim of the present study was to investigate in both qualitative and quantitative manner the composition of Cerebrospinal Fluid harvested form healthy newborn (5 days old) and young (30 and 50 days old) piglets using 1H NMR Spectroscopy, and to analyze any possible difference in metabolites concentration between age groups, related to age and Blood-BrainBarrier maturation.

PLOS ONE | DOI:10.1371/journal.pone.0157623 July 8, 2016

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Material and Methods Animals Animals used in this study were Large White x Landrace x Duroc commercial hybrids. The total amount of animals sampled for this study was 44: 17 5-days old piglets (P05), 18 30-days old piglets (P30) and 9 50-days old piglets (P50). None of the animals was sampled at two different time points. Pregnant sows (for P05 animals) and weaned piglets (for P30 and P50 animals) were delivered to our facility from the same farm (Societa' Agricola Pasotti S.s, Imola 40026, Italy) in order to obtain a population as consistent and coherent as possible. P05 animals were housed with the sow in the farrowing crate with a heating lamp, while P30 and P50 in multiple pens according to their age. Weaned animals (P30-P50) were fed an age-appropriate commercial diet twice a day. All animals were enrolled as negative controls or as pre-treatment individuals in different protocols approved by the Italian Ministry of Health (art.7, D.Lgs 116/92), and were monitored at least once a day by the veterinarian. The sampling procedure was performed under general anaesthesia in order to avoid stress and guarantee the welfare of the animals. All pigs were constantly monitored during and after the procedure to rule out any possible complication. According to the individuals’ protocols, all animals were eventually euthanized upon intravenous administration of Tanax (embutramide, mebenzonium iodide and tetracaine hydrochloride; 0.3 ml/kg; MSD Animal health, Milano, Italy) after general anesthesia.

Sampling procedure Animals were considered to be healthy on the basis of clinical examination and blood tests, including a Complete Blood Count (CBC) and Chemistry Profile. Sampling procedures were performed as previously described by Romagnoli et al. [17]. Briefly, animals were anesthetized using inhalational induction with 8% Sevoflurane (SevoFlo; Abbott Laboratories, Chicago, IL, USA) in a oxygen and air mixture (1:1). After endotracheal intubation, piglets were positioned in lateral recumbency, and the dorsal area of the neck was clipped and surgically prepared. Cisterna Magna was punctured using a 75mm 22gauge spinal needle, and 1 ml of clear, non-hemorrhagic Cerebrospinal Fluid was collected into a sterile cryogenic tube and immediately frozen in liquid nitrogen, then stored in a-80°C freezer until analysis.

NMR spectra acquisition and treatment The samples constituting each of the three groups were collected in two batches of similar size. The samples from each batch were prepared for 1H-NMR analysis simultaneously, to minimize possible variability due to preparation conditions. To meet the sample volume specifications of the NMR probe, 300 μl of CSF were added to 300 μl of distilled water. The samples were centrifuged for 15 minutes at 15,000 rpm at 4°C. 500 μl of supernatant were added to 100 μl of a D2O 1M phosphate buffer at pH 7.00 solution of 3-(trimethylsilyl)-propionic-2,2,3,3-d4 acid sodium salt (TSP) 6.25 mM, added as reference compound, and of 2 mM sodium azide, to avoid bacteria proliferation [18]. To minimize time at room temperature between sample preparation and spectra acquisition, the samples were stored at -20°C prior to analysis for a time varying between 12 and 24 hours. Immediately before spectra acquisition the samples were thawed and centrifuged again. The samples underwent analysis in random order, requiring a maximum of 6 hours. 1H-NMR spectra were recorded in 5 mm NMR tubes at 298 K with an AVANCE III spectrometer (Bruker, Milan, Italy) operating at 600.13 MHz. Following Öhman et al. [19], the signals from broad resonances originating from large molecules were suppressed by a CPMG-filter composed by 400 echoes with a τ of 400 μs and a

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180° pulse of 24 μs, for a total filter of 330 ms. The HOD residual signal was suppressed by means of presaturation. This was done by employing the cpmgpr1d sequence, part of the standard pulse sequence library. Each spectrum was acquired by summing up 256 transients using 32 K data points over a 7184 Hz spectral window, with an acquisition time of 2.28s. In order to apply NMR as a quantitative technique [20], the recycle delay was set to 5s, keeping into consideration the relaxation time of the protons under investigation. Pre-analytical sample management protocol and NMR experiments are conveniently summarized in S1 File, according to Rubtsov et al. guidelines [21]. The signals were assigned by comparing their chemical shift and multiplicity with Chenomx software (Chenomx Inc., Canada, ver 8.1) standard (ver. 10) and HMDB (ver. 2) data banks, as described in detail in S1A and S1B Fig. In case of ambiguity, proton-proton 2D experiments were performed, as shown in S1C Fig.

Data analysis Spectra were manually phase adjusted by means of Tospin (ver 3 –Bruker, Milan, Italy) and then transferred to Mestrenova (ver 10.0.2—Mestrelab Research S.L., Spain). Here a line broadening of 0.3 Hz was applied and an alignment towards TSP signal, set to 0 ppm, was applied. The baseline was adjusted by means of the Whittaker smoother algorithm [22], by applying a filter of 100 and a smoothing factor of 16384. Finally, the irregularities of the magnetic field leading to imperfections of the signals shape were compensated by reference deconvolution, by considering TSP singlet and a target linewidth of 1.2 Hz. No manual alignment of the signals was necessary, different to other body fluids [23]. Differences in water content among samples were taken into consideration by probabilistic quotient normalization [24], applied to the entire spectra array. Data analyses were performed on R environment (version 3.2.2; the R Foundation for Statistical Computing, Vienna, Austria). Molecules showing different concentrations between time points were analyzed using a non-parametric Mann-Whitney U test. A probability lower than 0.05 was considered as significant, adjusted for multiple comparisons through Bonferroni correction. Models of discriminant analysis based on projection on latent structures (PLS-DA) were built and graphically represented by means of the package mixOmics, formerly known as integrOmics [25]. For the purpose 75% of the samples from each group were randomly employed as a training set, while the remaining samples were used to test the model’s performance. The optimal number of new space components was found by 10 fold cross-validation. The trends in the individuals distribution were highlighted by representing them in the XY-variate subspace described by PLS-DA model. For each component, the importance of the molecules in the samples distribution was highlighted by calculating the correlation between each metabolite and the selected latent variable, thus obtaining the so called correlation circle plot. To rank the overall importance of each molecule in the model, we calculated its variable importance over projection (VIP) [26]. As an alternative criterion, PLS-DA models were built in their sparse version (sPLS-DA) [27]. Briefly, sPLS-DA algorithm does not build DA models on the entire set of molecules, but pre-selects only those with the highest discriminative power, thus indirectly acting as a molecules ranking procedure. In our case, for each iteration models of increasing complexity were built by adding one new molecule at each iteration to a starting number of two. The concentrations of the molecules observed in the present work spanned four orders of magnitude. The most concentrated molecules, with no biological reasons, would have dominated any multivariate model if employed as is. This forced us to scale each concentration to unit variance. This choice reduced the possibility for the reader to visually rank the molecules

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according to their importance in the models. Such drawback was solved by setting up a cascade analysis protocol, where each multivariate algorithm refined the information granted by the previous.

Results The sampling procedure proved to be strong and reliable, allowing the operator to collect blood-contamination free samples, suitable for analysis. Moreover, none of the animals showed alterations related to the procedure. All the raw data are showed in S1 Table. A 1D-NMR spectrum of CSF from a 30d pig, representative of all the spectra registered in the present work, is depicted in Fig 1. On each of the analyzed samples, 29 molecules could be observed above their limit of quantification, accounting for 95–98% of the total area of the spectra. Their concentration was obtained by integrating each spectrum over the ranges listed in Table 1, comprising complete multiplets, as in the case of lactate, or portions, as in the case of glucose. The concentration of the molecules quantified by NMR in CSF are reported in Table 2. To gain an overall first impression of how the samples spread in the 29 dimensions space, for each P05 sample we calculated the median euclidean distances from the other P05 samples and from the samples collected at P50. The so obtained intergroup/intragroup distance ratio resulted statistically higher than 1 (P